Atlantic Health and the Journey to AI-Native Care

2026-06-04 · Source: AI Magazine · Field: Health & Wellbeing — Healthcare Systems & Policy, Artificial Intelligence & Machine Learning, Medical Devices & Health Technology · Depth: Intermediate, quick

Summary

Atlantic Health, a leading not-for-profit healthcare provider serving 7.5 million people across 14 New Jersey counties with a 25,000-strong workforce and eight hospitals, is pursuing an "AI-native" strategy. Sunil Dadlani, EVP, Chief Information and Digital Officer, and Chief Cyber Security Officer, oversees this digital transformation. The vision involves deeply embedding intelligence into the organization's digital backbone, moving beyond isolated AI use cases to integrate AI across core clinical, operational, and financial workflows. This initiative aims to improve patient outcomes, enhance patient safety, ensure frictionless continuum of care, and make healthcare more accessible, equitable, and sustainable.

Key takeaway

For healthcare executives overseeing digital transformation, your strategy should prioritize embedding AI deeply into core workflows rather than treating it as an isolated add-on. Establish a unified digital backbone, robust data fabric, and strong governance framework first. This foundational work enables scaling AI across clinical, operational, and financial processes, ensuring improved patient outcomes and a truly "AI-native" care system.

Key insights

Becoming "AI-native" means deeply embedding intelligence into an organization's fabric, not merely adding AI as a feature.

Principles

• AI and digital transformation must ultimately improve patient outcomes.
• A unified digital backbone and robust data fabric are foundational for AI at scale.
• Shift from isolated AI use cases to embedded intelligence across workflows.

Method

The approach involves building a unified digital backbone, strong governance framework, and robust data fabric, then operationalizing this foundation across the enterprise to evolve into an AI-native system.

In practice

• Embed AI into core clinical workflows.
• Integrate AI into operational and financial processes.
• Prioritize AI execution at scale enterprise-wide.

Topics

• Healthcare AI
• Digital Transformation
• AI-Native Organizations
• Patient Outcomes
• Enterprise Technology
• Cybersecurity

Best for: Director of AI/ML, VP of Engineering/Data, Executive

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Magazine.